Optimization and Validation of Limit Check Error-Detection Performance Using a Laboratory-Specific Data-Simulation...

Autoverification procedures based on limit checks (LCs) provide important support to preanalytical, analytical, and postanalytical quality assurance in medical laboratories. A recently described method, based on laboratory-specific error-detection performances, was used to determine LCs for all chemistry analytes performed on random-access chemistry analyzers prior to application.

Using data sets of historical test results, error-detection simulations of limit checks were performed using the online MA Generator system (www.huvaros.com). Errors were introduced at various positions in the data set, and the number of tests required for an LC alarm to occur was plotted in bias detection curves. Random error detection was defined as an LC alarm occurring in 1 test result, whereas systematic error detection was defined as an LC alarm occurring within an analytical run, both with ≥97.5% probability. To enable the lower limit check (LLC) and the upper limit check (ULC) to be optimized, the simulation results and the LC alarm rates for specific LLCs and ULCs were presented in LC performance tables.

Optimal LLCs and ULCs were obtained for 31 analytes based on their random and systematic error-detection performances and the alarm rate. Reliable detection of random errors greater than 60% was only possible for analytes known to have a rather small variation of results. Furthermore, differences for negative and positive errors were observed.

The used method brings objectivity to the error-detection performance of LCs, thereby enabling laboratory-specific LCs to be optimized and validated prior to application.